{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.fftpack import fft,ifft\n",
    "import pandas as pd\n",
    "from scipy.optimize import minimize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# N=20取log bin的函数\n",
    "\n",
    "def databin_20(lst):\n",
    "    result = [[]]    \n",
    "    length = len(lst)\n",
    "    n = 0\n",
    "    for i in range(length):\n",
    "        result[-1].append(math.log(lst[i],10))\n",
    "        n = n+1\n",
    "        if n ==10:\n",
    "            n = 0\n",
    "            result.append([])\n",
    "    result.pop()\n",
    "    output=[]\n",
    "    for j in range(len(result)):\n",
    "        output.append(np.mean(result[j])) \n",
    "    return output\n",
    "\n",
    "def databin_20_std(lst):\n",
    "    result = [[]]\n",
    "    length = len(lst)\n",
    "    n = 0\n",
    "    for i in range(length):\n",
    "        result[-1].append(math.log(lst[i],10))\n",
    "        n = n+1\n",
    "        if n == 10:\n",
    "            n = 0\n",
    "            result.append([]) \n",
    "    result.pop()\n",
    "    output=[]\n",
    "    for j in range(len(result)):\n",
    "        output.append(np.std(result[j]))\n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data06 = pd.read_csv(\"0.3_10_tb200.csv\")\n",
    "'''data06['RATE'] = data06['RATE'].fillna(data092['RATE'].mean())   # 平均值插值'''\n",
    "data06['RATE'] = data06['RATE'].interpolate()\n",
    "\n",
    "\n",
    "dt=200\n",
    "counts_data = data06['RATE']\n",
    "N=len(counts_data)\n",
    "pnum = np.arange(len(counts_data))\n",
    "t = [i*dt for i in pnum]\n",
    "\n",
    "\n",
    "nf = N/2\n",
    "df = 1/(dt*N)\n",
    "F_a = np.arange(1,nf+1)\n",
    "F = [i*df for i in F_a]\n",
    "F1 = F[0:int(nf)]\n",
    "mean_x = np.mean(counts_data)\n",
    "dft = fft(counts_data)\n",
    "dft1= dft[1:int(nf)+1]\n",
    "per_data = (abs(dft1)**2)*2*dt/((mean_x**2)*N)\n",
    "p_times_f_data = np.multiply(np.array(F1),np.array(per_data))\n",
    "\n",
    "\n",
    "\n",
    "# 数据分bin\n",
    "F1_binned_06 = databin_20(F1)\n",
    "per_data_binned_06 = databin_20(per_data)\n",
    "per_data_b_std_06 = databin_20_std(per_data)\n",
    "p_times_f_data_b_06 = np.array(F1_binned_06)+np.array(per_data_binned_06)\n",
    "\n",
    "'''\n",
    "#减掉泊松噪声分bin\n",
    "per_nobs = [i-2/mean_x for i in per_data]\n",
    "F1_binned_06 = databin_20(F1)\n",
    "per_data_binned_06 = databin_20(per_nobs)\n",
    "per_data_b_std_06 = databin_20_std(per_nobs)\n",
    "p_times_f_data_b_06 = np.array(F1_binned_06)+np.array(per_data_binned_06)\n",
    "'''\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.scatter(F1_binned_06, p_times_f_data_b_06, color=\"r\", linewidth=1) \n",
    "plt.errorbar(F1_binned_06, p_times_f_data_b_06, yerr=per_data_b_std_06, fmt='.r')\n",
    "plt.xlabel(\"log frequency\")\n",
    "plt.ylabel(\"log power*frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data00 = pd.read_csv(\"0.3_10_tb200(00-2).csv\")  \n",
    "data00['RATE'] = data00['RATE'].interpolate()\n",
    "\n",
    "\n",
    "dt=200\n",
    "counts_data = data00['RATE']\n",
    "N=len(counts_data)\n",
    "pnum = np.arange(len(counts_data))\n",
    "t = [i*dt for i in pnum]\n",
    "\n",
    "\n",
    "nf = N/2\n",
    "df = 1/(dt*N)\n",
    "F_a = np.arange(1,nf+1)\n",
    "F = [i*df for i in F_a]\n",
    "F1 = F[0:int(nf)]\n",
    "mean_x = np.mean(counts_data)\n",
    "dft = fft(counts_data)\n",
    "dft1= dft[1:int(nf)+1]\n",
    "per_data = (abs(dft1)**2)*2*dt/((mean_x**2)*N)\n",
    "p_times_f_data = np.multiply(np.array(F1),np.array(per_data))\n",
    "\n",
    "\n",
    "\n",
    "# 数据分bin\n",
    "F1_binned_00 = databin_20(F1)\n",
    "per_data_binned_00 = databin_20(per_data)\n",
    "per_data_b_std_00 = databin_20_std(per_data)\n",
    "p_times_f_data_b_00 = np.array(F1_binned_00)+np.array(per_data_binned_00)\n",
    "\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.scatter(F1_binned_00, p_times_f_data_b_00, color=\"r\", linewidth=1) \n",
    "plt.errorbar(F1_binned_00, p_times_f_data_b_00, yerr=per_data_b_std_00, fmt='.r')\n",
    "plt.xlabel(\"log frequency\")\n",
    "plt.ylabel(\"log power*frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data00 = pd.read_csv(\"0.3_10_tb200(00).csv\")  \n",
    "data00['RATE'] = data00['RATE'].interpolate()\n",
    "\n",
    "\n",
    "dt=200\n",
    "counts_data = data00['RATE']\n",
    "N=len(counts_data)\n",
    "pnum = np.arange(len(counts_data))\n",
    "t = [i*dt for i in pnum]\n",
    "\n",
    "\n",
    "nf = N/2\n",
    "df = 1/(dt*N)\n",
    "F_a = np.arange(1,nf+1)\n",
    "F = [i*df for i in F_a]\n",
    "F1 = F[0:int(nf)]\n",
    "mean_x = np.mean(counts_data)\n",
    "dft = fft(counts_data)\n",
    "dft1= dft[1:int(nf)+1]\n",
    "per_data = (abs(dft1)**2)*2*dt/((mean_x**2)*N)\n",
    "p_times_f_data = np.multiply(np.array(F1),np.array(per_data))\n",
    "\n",
    "\n",
    "\n",
    "# 数据分bin\n",
    "F1_binned_00 = databin_20(F1)\n",
    "per_data_binned_00 = databin_20(per_data)\n",
    "per_data_b_std_00 = databin_20_std(per_data)\n",
    "p_times_f_data_b_00 = np.array(F1_binned_00)+np.array(per_data_binned_00)\n",
    "\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.scatter(F1_binned_00, p_times_f_data_b_00, color=\"r\", linewidth=1) \n",
    "plt.errorbar(F1_binned_00, p_times_f_data_b_00, yerr=per_data_b_std_00, fmt='.r')\n",
    "plt.xlabel(\"log frequency\")\n",
    "plt.ylabel(\"log power*frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data091 = pd.read_csv(\"0.3_10_tb200(091).csv\")  \n",
    "data091['RATE'] = data091['RATE'].interpolate()\n",
    "\n",
    "\n",
    "dt=200\n",
    "counts_data = data091['RATE']\n",
    "N=len(counts_data)\n",
    "pnum = np.arange(len(counts_data))\n",
    "t = [i*dt for i in pnum]\n",
    "\n",
    "\n",
    "nf = N/2\n",
    "df = 1/(dt*N)\n",
    "F_a = np.arange(1,nf+1)\n",
    "F = [i*df for i in F_a]\n",
    "F1 = F[0:int(nf)]\n",
    "mean_x = np.mean(counts_data)\n",
    "dft = fft(counts_data)\n",
    "dft1= dft[1:int(nf)+1]\n",
    "per_data = (abs(dft1)**2)*2*dt/((mean_x**2)*N)\n",
    "p_times_f_data = np.multiply(np.array(F1),np.array(per_data))\n",
    "\n",
    "\n",
    "\n",
    "# 数据分bin\n",
    "F1_binned_091 = databin_20(F1)\n",
    "per_data_binned_091 = databin_20(per_data)\n",
    "per_data_b_std_091 = databin_20_std(per_data)\n",
    "p_times_f_data_b_091 = np.array(F1_binned_091)+np.array(per_data_binned_091)\n",
    "\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.scatter(F1_binned_091, p_times_f_data_b_091, color=\"r\", linewidth=1) \n",
    "plt.errorbar(F1_binned_091, p_times_f_data_b_091, yerr=per_data_b_std_091, fmt='.r')\n",
    "plt.xlabel(\"log frequency\")\n",
    "plt.ylabel(\"log power*frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TT56s7949Wd8BV4+uOeVHd/+bqvpXSV6Q5Mzu/t+3riwAgDXs0Hnc1hsevbW7/0uSj21FMQAArG690PaGqrq+u9+wJdUAG3NkgskrrphMMLkD5ikC2KnWu2H8l5JcU1Xb/5IMGM0OnmAStjV/jLGG9XrakuQBST5YVb9bVW85ssy7MGAdO3iCSdi2/DHGMcxyw/h/M/cqgI3bwRNMwrZ1rD/GdsDVkRzbuj1t3X1FkhuT/L3p4/dnclsrYJF28ASTsG35Y4xjWDe0VdWPJPmtJK+eNj0oyX+bZ1HADHbwBJOwbfljjGOY5Zy2H0vy5CSfS5Lu/lCS+86zKGAGO3iCSdi2/DHGMcxyTtsd3f35qkqSVNVJSXquVQGz2aETTMK2deSPrgsumFyMsHv3JLD5Y2zjtuF34iw9bVdU1c8m+aqq+vYkb0zy2/MtCwB2qH37kic+MXnKU5Ibb5wtsJkmZEeYJbT9dJLbklyX5J8kuTTJy+ZZFAAwI9OE7BizhLazkxzs7u/t7ud298XdbXgUAJaBORs3btCeyVnOafuhJK+qqk8m+f3p8p7u/vQ8CwMAZmCakI1Zq2cyWfpzB2eZp+0Hu/thSZ6T5JYkv57JcCkAsGimCdmYgXsmZ5mn7QVV9epM5mo7J8mvJflH8y4MAJiBaUI2ZuCeyVnOafuVJGcluTjJS7r7f+vu9863LADYoLPPniw7zbLM2TjKeWID90zOMjx6epIXJrlnkouq6n1V9bq5VwYAo9rqAHM804RsppGuYB24Z3KW4dGvSbIrye4ke5J8bZIvnegLV9Urquraqrq6qt5RVQ9cY7vzq+pD0+X8E31dAJirkQLMZhnpPLFl6Zk8DrMMj74nyXcnuTbJ87r74d29GeHpld396O4+K8lbk/zc0RtU1WlJXp7kHyZ5QpKXV9Wpm/DaADAfIwWYzTLaeWKL7pk8TutO+dHdj06Sqrp3NvH2Vd39uRWr91pj39+R5PLu/tS0hsuTPD3JGzarDgDYVKMFmM2wa9ekR3G1djbNLMOj31RVf5Tk+iR/XFVXVdU3bcaLV9VFVfXRJPuySk9bkgcl+eiK9Vumbavt68KqOlxVh2+7zYwkACzIwCe6H7eBzxMbySzDoweS/Ivu3t3du5L8y2nbuqrqnVV1/SrLM5Oku/d394OTHEzy4tV2sUrbqr193X2gu/d2994zzjhjlvIA4K5O9CKCnRhgBj5PbCSz3BHhXt39riMr3X2oqu41y867+5wZ63h9krdlcv7aSrdkchutI85McmjGfQLAxmzGbPlHtrvggsl+du+eBLaRA8yhQ+tvs29fcvHFs2/Phs3S0/aRqvrXVbVnurwsyZ+f6AtX1UNXrJ6X5IZVNrssyblVder0AoRzp20AsPk26yKCQU90Z7nNEtpemOSMJP81ySXTxz+8Ca/9i9Oh0mszCWM/niRVtbeqfiNJphcgvCLJ+6fLLxy5KAEANt1OvIiAYcxy9eink7ykqr42yZe6+68344W7+zlrtB9O8qIV669J8prNeE0AOCZXQbLEZrl69Jur6rok1yS5rqquqarHz780ANhiO/EiAoYxy/Dov0/yv3b3nu7ek+THkvyHuVYFAIvgKkiW2CxXj/51d//+kZXufk9VbcoQKQAsHVdBsqT/3WcJbe+rqldncheCTvK8JIeq6nFJ0t0fmGN9AABkttB21vTfo+dQ+5ZMQty3bWpFAADcxSxXjz51KwoBAHaIJR1+XHazXIgAwLyc6C2TgB1DaANYlLVumSS4AasQ2gAWZbNumQTsCOue01ZVz16l+bNJruvuT2x+SQDH6chQ4x13TIYal/0m3W6ZBGzALFePXpDkSUneNV0/O8mVSR5WVb/Q3a+bU20As1trqDFZ3uDmlknABswyPPqlJN/Y3c+Z3i/0kUnuSPIPk/zUPIsDmNmIQ41umcSiuABmSLOEtj3d/Zcr1j+R5GHd/akkfzefsgA2aMShxmW5ZZJf4DuLC2CGNcvw6O9X1VuTvHG6/twk766qeyX5zNwqG4F5ZmB5jDrUuOhbJo04rMyJOVavtP/mS22WnrYjN4g/K8ljk7w2yY9199+aeBdYGoYaj8+Iw8qcmBF7pUky2x0Ruqrek+Tzmdy26n3d3XOvDGAjjvQQXHDBpNdo9+7lv3p0GfgFvvOM2ivN+j1tVfV9Sd6XybDo9yX5w6p67rwLA9iwffuSJz4xecpTkhtvFNhmsdYvar/Aty+90sOaZXh0f5Jv7u7zu/sHkzwhyb+eb1kAbAm/wHeeZbkAhg2bJbTd7ahJdD85488BsOy2+he4K1WXg17pIc1y9ejbq+qyJG+Yrj8vyaXzKwmALbVVV7C6UhVOyLo9Zt39r5IcSPLoJI9JcqC7TaoLwMa4UhVOyCw9benuNyV505xrAWA7c6UqnJA1Q1tV/XUmU3zc5alMZgL5mrlVBcD2Y6oJOCFrhrbuvvdWFgLANnfRRZNz2FYOkbpSlbW469BduAoUgK1hqgk4ITOd0wYAm2LR91qFgelpAwAYgNAGADAAoQ0AYADOaQMANofzFOdKTxsAwACENgCAAQhtAAADENoAAAYgtAEADEBoAwAYgNAGADAA87QBwLIx3xmr0NMGADAAoQ0AYABCGwDAAIQ2AIABCG0AAAMQ2gAABiC0AQAMQGgDABiA0AYAMAChDQBgAEIbAMAA3HsUALYD9yvd9vS0AQAMQGgDABiA0AYAMAChDQBgAEIbAMAAhDYAgAEIbQAAAzBPGwCw8ww4r52eNgCAAQhtAAADMDwKAIxnwOHNE6WnDQBgAAvpaauqVyR5ZpIvJflEkh/q7ltX2e6LSa6brt7c3edtXZUAAMtjUT1tr+zuR3f3WUnemuTn1tju/+vus6aLwAYA7FgLCW3d/bkVq/dK0ouoAwBgFAs7p62qLqqqjybZl7V72u5ZVYer6sqqetY6+7twuu3h2267bdPrBQBYpLmFtqp6Z1Vdv8ryzCTp7v3d/eAkB5O8eI3d7OruvUl+IMmvVNXXr/V63X2gu/d2994zzjhj098PAGzIoUM78gpH5mduFyJ09zkzbvr6JG9L8vJV9nHr9N+PVNWhJI9N8mebVSMAwCgWMjxaVQ9dsXpekhtW2ebUqjp5+vj0JE9O8sdbUyEAwHJZ1OS6v1hVD89kyo+bkvzTJKmqvUn+aXe/KMk3Jnl1VX0pk3D5i90ttAEAO9JCQlt3P2eN9sNJXjR9/N+TPGor6wIAWFZuYwWAE+ZhAEIbABxNiGUJufcoAMAA9LQBwE6kN3E4etoAAAYgtAEwvoMHkyuvTK64ItmzZ7IO24zhURidIQ52uoMHkwsvTO64Y7J+002T9STZt29xdcEm09MGwNj2709uv/3ObbffPmmHbURPG8Ci6S09MTffvLF2GJSeNgDGtmvXxtphUEIbAGO76KLklFPu3HbKKZN22EaENgDGtm9fcuBAcvLJk/XduyfrLkJgm3FOGwDj27cvufjiyWPnCLJN6WkDABiA0AYAMAChDQBgAEIbAMAAhDYAgAEIbQAAAzDlBwBby5QccFz0tAEADEBoAwAYgNAGADAAoQ0AYABCGwDAAIQ2AIABCG0AAAMQ2gAABiC0AQAMQGgDABiA0AYAMAChDQBgAEIbAMAAhDYAgAEIbQAAAxDaAAAGILQBAAxAaAMAGMBJiy4AYFMdOrToCgDmQk8bAMAAhDYAgAEIbQAAAxDaAAAGILQBAAxAaAMAGIDQBgAwAKENAGAAQhsAwACENgCAAQhtAAADENoAAAYgtAEADEBoAwAYgNAGADAAoQ0AYABCGwDAAIQ2AIABCG0AAAMQ2gAABiC0AQAMQGgDABiA0AYAMICFh7aqemlVdVWdvsbz51fVh6bL+VtdHwDAMjhpkS9eVQ9O8u1Jbl7j+dOSvDzJ3iSd5Kqqekt3f3rrqgQAWLxF97T9cpKfzCSQreY7klze3Z+aBrXLkzx9q4oDAFgWCwttVXVeko919zXH2OxBST66Yv2Wadtq+7uwqg5X1eHbbrttEysFAFi8uQ6PVtU7k9x/laf2J/nZJOeut4tV2lbtlevuA0kOJMnevXvX6rkDABjSXENbd5+zWntVPSrJQ5JcU1VJcmaSD1TVE7r74ys2vSXJ2SvWz0xyaC7FAgAssYUMj3b3dd193+7e0917MglnjzsqsCXJZUnOrapTq+rUTHrmLtvicgEAFm7RFyLcRVXtrarfSJLu/lSSVyR5/3T5hWkbAMCOstApP46Y9rYdeXw4yYtWrL8myWsWUBYAwNJYup42AADuSmgDABiA0AYAMAChDQBgAEIbAMAAhDYAgAEIbQAAAxDaAAAGILQBAAxAaAMAGIDQBgAwAKENAGAAQhsAwACENgCAAQhtAAADENoAAAYgtAEADEBoAwAYgNAGADAAoQ0AYABCGwDAAIQ2AIABCG0AAAMQ2gAABiC0AQAMQGgDABjASYsuAAA2xaFDi64A5kpPGwDAAIQ2AIABCG0AAAMQ2gAABiC0AQAMQGgDABiA0AYAMAChDQBgAEIbAMAAhDYAgAEIbQAAAxDaAAAGILQBAAxAaAMAGIDQBgAwAKENAGAAQhsAwACENgCAAQhtAAADqO5edA2brqpuS3LTFr3c6Un+aotea7twzDbOMds4x2zjHLONc8w2zjG7q93dfcZ6G23L0LaVqupwd+9ddB0jccw2zjHbOMds4xyzjXPMNs4xO36GRwEABiC0AQAMQGg7cQcWXcCAHLONc8w2zjHbOMds4xyzjXPMjpNz2gAABqCnDQBgAEIbAMAAhLYNqKqXVlVX1emrPHdWVb23qj5YVddW1fNWPPcfq+rPq+rq6XLW1la+OMc6ZtPnz6+qD02X81e0P76qrquqD1fVr1ZVbV3Vi1FVr5h+dq6uqndU1QNX2eapKz5HV1fV/6yqZ02f23Gfs1mO2XS7L644Lm9Z0f6QqvrD6efv/62qe2xd9Ysx4+fM99kKG/ic+T6bqqpXVtUN0+N2SVXdZ5VtHn7U99nnquonps/9fFV9bMVzz9j6d7GEutsyw5LkwUkuy2TS3tNXef5hSR46ffzAJH+R5D7T9f+Y5LmLfg9LeMxOS/KR6b+nTh+fOn3ufUmelKSS/E6S71z0+9mC4/U1Kx6/JMmr1tn+tCSfSnLKdH3Hfc5mPWZJ/maN9t9M8vzp41cl+dFFv6dlOGa+z47rmPk+u/PxODfJSdPHv5Tkl9bZ/u5JPp7JJLNJ8vNJXrro97Fsi5622f1ykp9MsuqVG939p939oenjW5N8Ism6sxtvc8c8Zkm+I8nl3f2p7v50ksuTPL2qHpDJl+R7e/J/739K8qwtqXiBuvtzK1bvlbWP2xHPTfI73X37/KpabsdxzL5s2tvxbUl+a9r02vicHdnG99kKM37OfJ+t0N3v6O4vTFevTHLmOj/ytCR/1t1bdTejIQltM6iq85J8rLuvmXH7JyS5R5I/W9F80bSb+Jer6uR51LlMZjxmD0ry0RXrt0zbHjR9fHT7tldVF1XVR5PsS/Jz62z+/CRvOKptR33OkpmP2T2r6nBVXXlkODnJ1yX5zIpfLD5nq2+747/PkpmOme+ztb0wkx7GY1nt++zF08/Za6rq1PmUNhahbaqq3llV16+yPDPJ/qz/C/TIfh6Q5HVJfri7vzRt/pkkj0jyzZl0nf/UHN7CltuEY7baeR19jPbhrXPM0t37u/vBSQ4mefEx9vOAJI/KZPj5iJ34OZv1mO3qyW1zfiDJr1TV18fnbNbPme+zzHTMfJ8ddcym2+xP8oVMjtta+7lHkvOSvHFF8/+T5OuTnJXJ8Pz/MZc3MZiTFl3Asujuc1Zrr6pHJXlIkmum546emeQDVfWE7v74Udt+TZK3JXlZd1+5Yt9/MX14R1X9hyQvncNb2HKbcMxuSXL2ivUzkxyatp95VPutm1b4Aq11zFbx+kw+Sy9f4/nvS3JJd//din3vqM/ZKtY8ZtMhvnT3R6rqUJLHJnlTkvtU1UnT3jafsxV8n61prWPm++wo04sxvivJ06ZDw2v5ziQf6O6/XLHvLz+uqouTvPUEy90W9LSto7uv6+77dvee7t6Tyf+Aj1slsN0jySVJ/lN3v/GUFTNZAAAE10lEQVSo5x4w/bcyOZfh+i0pfkFmPWaZ9BKdW1WnTru+z01y2fSXwl9X1ROnx+wHk7x5K9/DIlTVQ1esnpfkhmNs/v05aihhp33OktmO2fTzdfL08elJnpzkj6e/RN6VybmBSXJ+fM6ObOP7bIUZ/9/0fbZCVT09k17Y82Y473bN77Op78kO+JzNZNFXQoy2JLkx0yshk+xN8hvTxy9I8ndJrl6xnDV97veSXJfJh+4/J/nqRb+PZThm0/UXJvnwdPnhFe17p8frz5L8WqZ379jOSyY9P9cnuTbJbyd50BrHbE+SjyW521E/v+M+Z7McsyTfMj0u10z/vWDFz//9TK7s+3AmQzMnL/o9Lckx8322wWM2Xfd99pX3/OFMzvE78vl51bT9gUkuXbHdKUk+meRrj/r5100/Z9cmeUuSByz6PS3D4jZWAAADMDwKADAAoQ0AYABCGwDAAIQ2AIABCG0AAAMQ2oClV1V/s0n7eURVXV1VfzS9KwLAMIQ2YCd5VpI3d/dju/vL99KsCd+HwFLzJQUMYxquXjm9v+F1VfW8afvdqurfVdUHq+qtVXVpVT33qJ99RpKfSPKiqnpXVe2pqj+pqn+X5ANJHlxV51bVe6vqA1X1xqr66unPPr2qbqiq91TVr1bVW6ftP19VL13xGtdX1Z7p4xdU1fumPXuvrqq7T9v/piY3H7+mJjewv9+0/X5Vdcm0/Zqq+paqekVV/fiK/V9UVS+Z3xEGlpnQBozk2ZncQPoxSc5J8srp7W6encmdIh6V5EVJnnT0D3b3pUleleSXu/up0+aHZ3Krpscm+dskL0tyTnc/LsnhJP+iqu6Z5OIk353kHyW5/3pFVtU3Jnlekid391lJvphk3/TpeyW5srsfk+TdSX5k2v6rSa6Ytj8uyQeT/PtMbq+VaU/g83OMG28D25sbxgMj+V+SvKG7v5jkL6vqiiTfPG1/Y3d/KcnHq+pdM+7vpv7KzdCfmOSRSf5gcovI3CPJe5M8Ismfd/eHkqSq/nOSC9fZ79OSPD7J+6f7+qokn5g+9/l85ebXVyX59unjb8vkvpSZvr/PJvlsVX2yqh6b5H5J/qi7PznjewO2GaENGEltsH09f3vUPi7v7u+/046rzkqy1v3+vpA7j1jcc8W+XtvdP7PKz/xdf+X+gV/M+t/Dv5HkhzLp4XvNOtsC25jhUWAk707yvKq6e1WdkeRbM7nh+3uSPGd6btv9kpx9HPu+MsmTq+obkqSqTqmqhyW5IclDVlxtujLU3ZjJUGaq6nFJHjJt/90kz62q+06fO62qdq/z+r+b5Een29+9qr5m2n5Jkqdn0qN42XG8L2CbENqAkVyS5Nok1yT5vSQ/2d0fT/KmJLckuT7Jq5P8YSbDizPr7tsy6dF6Q1Vdm0mIe0R3/89MhkPfVlXvSXLTih97U5LTqurqTALXn0739ceZnB/3jum+Lk/ygHVK+PEkT62q6zIZNv0H0319Psm7kvzmdNgU2KHqK730AOOqqq/u7r+pqq/LpPftydNAt9mvc3aSl3b3d232vtd4vbtlcnXr9x45rw7YmZzTBmwXb62q+2RyAcEr5hHYtlpVPTKTixYuEdgAPW0AAANwThsAwACENgCAAQhtAAADENoAAAYgtAEADOD/B8NHVtstVwpDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data092 = pd.read_csv(\"0.3_10_tb200(092).csv\")  \n",
    "data092['RATE'] = data092['RATE'].interpolate()\n",
    "\n",
    "\n",
    "dt=200\n",
    "counts_data = data092['RATE']\n",
    "N=len(counts_data)\n",
    "pnum = np.arange(len(counts_data))\n",
    "t = [i*dt for i in pnum]\n",
    "\n",
    "\n",
    "nf = N/2\n",
    "df = 1/(dt*N)\n",
    "F_a = np.arange(1,nf+1)\n",
    "F = [i*df for i in F_a]\n",
    "F1 = F[0:int(nf)]\n",
    "mean_x = np.mean(counts_data)\n",
    "dft = fft(counts_data)\n",
    "dft1= dft[1:int(nf)+1]\n",
    "per_data = (abs(dft1)**2)*2*dt/((mean_x**2)*N)\n",
    "p_times_f_data = np.multiply(np.array(F1),np.array(per_data))\n",
    "\n",
    "\n",
    "\n",
    "# 数据分bin\n",
    "F1_binned_092 = databin_20(F1)\n",
    "per_data_binned_092 = databin_20(per_data)\n",
    "per_data_b_std_092 = databin_20_std(per_data)\n",
    "p_times_f_data_b_092 = np.array(F1_binned_092)+np.array(per_data_binned_092)\n",
    "\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.scatter(F1_binned_092, p_times_f_data_b_092, color=\"r\", linewidth=1) \n",
    "plt.errorbar(F1_binned_092, p_times_f_data_b_092, yerr=per_data_b_std_092, fmt='.r')\n",
    "plt.xlabel(\"log frequency\")\n",
    "plt.ylabel(\"log power*frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.errorbar(F1_binned_00, p_times_f_data_b_00, yerr=per_data_b_std_00, fmt='o', label='2000')\n",
    "plt.errorbar(F1_binned_06, p_times_f_data_b_06, yerr=per_data_b_std_06, fmt='^', label='2006')\n",
    "plt.errorbar(F1_binned_091, p_times_f_data_b_091, yerr=per_data_b_std_091, fmt='s',label='2009(1)')\n",
    "plt.errorbar(F1_binned_092, p_times_f_data_b_092, yerr=per_data_b_std_092, fmt='*',label='2009(2)')\n",
    "plt.xlabel(\"log frequency\")\n",
    "plt.ylabel(\"log power*frequency\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.errorbar(F1_binned_00, per_data_binned_00, yerr=per_data_b_std_00, fmt='o', label='2000')\n",
    "plt.errorbar(F1_binned_06, per_data_binned_06, yerr=per_data_b_std_06, fmt='^', label='2006')\n",
    "plt.errorbar(F1_binned_091, per_data_binned_091, yerr=per_data_b_std_091, fmt='s',label='2009(1)')\n",
    "plt.errorbar(F1_binned_092, per_data_binned_092, yerr=per_data_b_std_092, fmt='*',label='2009(2)')\n",
    "plt.xlabel(\"log frequency\")\n",
    "plt.ylabel(\"log power\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
